Multi-station dynamic environment monitoring method and system

Abstract

The invention relates to the technical field of power environment monitoring, in particular to a multi-station power environment monitoring method and system, and the method specifically comprises the steps: collecting the power environment monitoring data of each machine room of each railway station; the method comprises the following steps: determining a corresponding trend condition by analyzing real-time change characteristics of power environment monitoring data in machine rooms of different stations, predicting a monitoring value at a future moment in combination with a development trend of the monitoring data, and determining a comprehensive alarm triggering degree of each station based on a difference between the predicted value and a preset alarm threshold value of each monitoring data. Determining the alarm attribute of each station; the maintenance resources are regulated and controlled through the corresponding alarm mechanisms according to the early warning information of different stations, multi-station dynamic environment monitoring is achieved, the problem that the dynamic environment monitoring effect is poor due to the fact that the alarm mechanisms are unreasonable is solved, and the overall elasticity and management efficiency of station management are enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of power environment monitoring, and in particular to a multi-site power environment monitoring method and system. Background Art

[0002] Multi-site power environment monitoring is designed to monitor the power environment parameters of multiple sites at the same time, such as temperature, humidity, power status, safety status, etc. Its core goal is to enable managers to grasp the operating status of each site in real time through remote monitoring and intelligent analysis, and to detect and deal with potential problems as early as possible. When abnormal detection data is found, the alarm module triggers the corresponding alarm mechanism to notify managers for maintenance, thereby ensuring the stable operation of the site. In the current railway transportation system, the configuration and purpose of the machine room at different sites may be different. There are multiple machine rooms at a site. At present, the early warning strategies for different sites are divided into separate alarms and centralized alarms.

[0003] Individual alarm means that the alarm information of each site is only sent to the relevant management personnel of the site, while centralized alarm means that the alarm information of all sites is centralized in a central monitoring system for processing and management, and the central monitoring system will analyze, record, regulate and take corresponding measures. At present, the monitoring and early warning of the power environment monitoring data of different sites is generally based on the individual alarm strategy, that is, each site independently manages its own alarm information, and the alarm information is directly processed and sent by the site, and the local management personnel are directly notified for maintenance. However, since individual alarms are not interconnected and cannot share information and data, the alarm events of different monitoring points cannot be effectively correlated and analyzed, which reduces the overall effect of multi-site power environment monitoring. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a multi-site power environment monitoring method and system, and the technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a multi-site power environment monitoring method, the method comprising the following steps:

[0006] Collect power environment monitoring data of each computer room at each site;

[0007] Determine the data trend degree of each monitoring data in each computer room based on the change trend of each monitoring data in each computer room; determine the development trend degree of each monitoring data in each computer room at each moment based on the data trend degree of each monitoring data in each computer room and the local data change situation;

[0008] Determine the predicted value of each monitoring data in each computer room at the next moment based on the numerical value of each monitoring data in each computer room at each moment and the corresponding development trend degree; determine the alarm triggering degree of each computer room at each moment based on the difference between the predicted value and the preset alarm threshold of various monitoring data, and calculate the comprehensive alarm triggering degree of each site;

[0009] Critical sites and common sites are distinguished based on the comprehensive alarm triggering degree of each site; alarm mechanisms are used to monitor and control critical sites and common sites respectively.

[0010] In one embodiment, the process of acquiring the data trend of each monitoring data of each computer room is as follows:

[0011] For any type of monitoring data of each computer room, the difference between each moment and the previous moment of the monitoring data is calculated and recorded as the first difference; the average of all the first differences of the monitoring data is taken as the data trend of the monitoring data.

[0012] In one embodiment, the expression of the development trend degree of each monitoring data of each computer room at each moment is:

[0013] In the formula, G k is the development trend of any monitoring data in any computer room at the kth moment, A k is the actual value of any of the monitoring data at the kth moment, is the mean of all historical data of any one type of monitoring data before the kth moment, and Y is the data trend of any one type of monitoring data.

[0014] In one embodiment, the expression for the predicted value of each monitoring data of each computer room at the next moment is:

[0015] A′ k+1 =A k ×(1+G′ k ), where A′ k+1 is the predicted value of any monitoring data at the k+1th moment, A k is the actual value of any monitoring data at the kth moment, G′ k It is the normalized value of the development trend degree of any one of the monitoring data at the kth moment.

[0016] In one embodiment, the expression of the alarm triggering degree of each computer room at each time is:

[0017] In the formula, is the alarm triggering degree of the a-th computer room at the k-th time; N is the number of monitoring data types of each computer room; is the predicted value of the b-th monitoring data of the a-th computer room at the k+1th time; μ a,b It is the preset alarm threshold of the bth type of monitoring data in the ath computer room.

[0018] In one embodiment, the expression of the comprehensive alarm triggering degree of each site is:

[0019] The comprehensive alarm triggering degree of each site at each moment is determined based on the fusion value of the alarm triggering degree of all computer rooms in each site at each moment.

[0020] In one embodiment, the calculation expression of the comprehensive alarm triggering degree is:

[0021] In the formula, is the comprehensive alarm triggering degree of any site at the kth moment, S is the number of computer rooms included in any site, is the alarm triggering degree of the ith computer room in any of the sites at the kth moment, and exp() is an exponential function with the natural number e as the base.

[0022] In one of the embodiments, the distinction between key sites and ordinary sites is based on the comprehensive alarm triggering degree of each site, specifically: sites with a normalized value of the comprehensive alarm triggering degree greater than or equal to a preset first threshold are regarded as key sites, and sites with a normalized value less than the preset first threshold are regarded as ordinary sites.

[0023] In one embodiment, the critical sites and common sites are monitored and controlled respectively by using alarm mechanisms, specifically: critical sites are monitored and controlled by using separate alarms, and common sites are monitored and controlled by using centralized alarms.

[0024] In a second aspect, an embodiment of the present application also provides a multi-site power environment monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0025] The embodiments of the present application have at least the following beneficial effects:

[0026] The present application optimizes the problem of high management and maintenance costs caused by the inability to conduct effective correlation analysis of alarm events at different sites when issuing early warnings for different sites based on separate alarm strategies. Real-time data prediction is performed by analyzing the trends of monitoring data at different sites, and the comprehensive alarm triggering degree of each site is determined based on the difference between the prediction results and the preset alarm thresholds of various monitoring data. Then, based on the difference in the comprehensive alarm triggering degree, different alarm strategies are set for different stations by combining separate alarms and centralized alarm methods. Corresponding alarm mechanisms are determined for different sites for early warning, ensuring the rapid response and autonomy of key sites while improving the synchronization of unified site management and comprehensive analysis, thereby enhancing the overall flexibility and management efficiency of railway site management. Maintenance resources are regulated through corresponding alarm mechanisms based on the early warning information of different sites, and multi-site power environment monitoring is realized, thus avoiding the problem of poor power environment monitoring effect due to unreasonable alarm mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of a multi-site power environment monitoring method provided in one embodiment of the present application;

[0029] Figure 2 This is a schematic diagram of the process of obtaining the data trend of each type of monitoring data in each computer room. DETAILED DESCRIPTION

[0030] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of a multi-site power environment monitoring method and system proposed in accordance with the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0031] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0032] The following is a detailed description of a multi-site power environment monitoring method and system provided by the present application in conjunction with the accompanying drawings.

[0033] See also Figure 1 , which shows a flowchart of a multi-site power environment monitoring method provided by an embodiment of the present application, the method comprising the following steps:

[0034] Step S1, collecting the power environment monitoring data of each computer room at each site.

[0035] Sensors in each computer room at different sites monitor and collect various power environment parameters of each computer room at different sites in real time, including temperature, humidity, power status, and access control status. Based on the trend characteristics of these power environment monitoring data, alarm strategies for different sites are determined to improve site management efficiency.

[0036] Preferably, in one embodiment of the present application, for the power environment monitoring data collection of each site computer room, the data collection time interval is set to 1 second. As other embodiments of the present application, the implementer can set the data collection time interval according to the actual situation.

[0037] Step S2, determining the data trend degree of each monitoring data in each computer room based on the change trend of each monitoring data in each computer room; determining the development trend degree of each monitoring data in each computer room at each moment based on the data trend degree of each monitoring data in each computer room and the local data change situation.

[0038] Since different alarm mechanisms have different response capabilities to the maintenance of each station, and different stations in actual railway transportation have different response requirements for maintenance, some large-scale key stations may require faster maintenance speed and more maintenance resources, while some smaller ordinary stations have a lower maintenance priority than key stations. Because stations with different attributes are determined based on the real-time data of the station, the corresponding alarm mechanism is determined based on the station attributes later.

[0039] The traditional threshold-based early warning trigger mechanism has a lag in actual monitoring, which causes maintenance personnel to be unable to respond to abnormal conditions immediately, resulting in adverse consequences. Therefore, this embodiment predicts data through the development trend of real-time monitoring data, and determines the early warning trigger time based on the prediction results.

[0040] (1) Based on the changing trend of various power environment monitoring data of each computer room, the data trend degree of various power environment monitoring data of each computer room is calculated, and the expression is:

[0041] Where Y a,b is the data trend of the bth monitoring data of the ath computer room, M is the number of various monitoring data collected at the current moment, are the values ​​of the bth type of monitoring data collected at the kth and k-1th moments in the ath computer room. is the first difference.

[0042] The overall degree of change of the data is measured by the data difference between two adjacent moments. If the data trend is greater than zero, it means that the data shows an overall growth trend. If the data trend is less than zero, it means that the data shows an overall downward trend.

[0043] (2) Based on the data trend of various power environment monitoring data of each computer room and the local data change, the development trend of various power environment monitoring data of each computer room at each moment is calculated, and the expression is:

[0044] In the formula, G k is the development trend of any monitoring data in any computer room at the kth moment, A k is the actual value of any of the monitoring data at the kth moment, is the mean of all historical data of any one type of monitoring data before the kth moment, and Y is the data trend of any one type of monitoring data.

[0045] The larger the value is, the greater the local variation of the data is, and the more obvious the trend change is. k The larger the value is, the greater the growth trend of any monitoring data at that moment is; k The smaller it is, the greater the downward trend of any monitoring data at that moment.

[0046] (3) Normalize the development trend of all types of monitoring data of all computer rooms at all times, and the normalization range is [-1, 1]. Among them, for the normalization of the development trend, this application adopts the maximum normalization algorithm to normalize the development trend. There are many existing normalization algorithms, and the implementer can also use other normalization algorithms to normalize the development trend. This application does not make specific restrictions.

[0047] Step S3, based on the numerical value of each monitoring data in each computer room at each moment and the corresponding development trend, determine the predicted value of each monitoring data in each computer room at the next moment; based on the difference between the predicted value and the preset alarm threshold of various monitoring data, determine the alarm triggering degree of each computer room at each moment, and calculate the comprehensive alarm triggering degree of each site.

[0048] Since the traditional threshold-based abnormal data warning mechanism has a lag, it is possible to make predictions based on the trend value of real-time data, and thus determine the warning triggering degree of the corresponding computer room based on the prediction results.

[0049] (1) Based on the numerical values ​​of various monitoring data of each computer room at each moment and the corresponding development trend, the predicted values ​​of various monitoring data of each computer room at the next moment are calculated. The expression is:

[0050] A′ k+1 =A k ×(1+G′ k ), where A′ k+1 is the predicted value of any monitoring data at the k+1th moment, A k is the actual value of any monitoring data at the kth moment, G′ k It is the normalized value of the development trend degree of any one of the monitoring data at the kth moment.

[0051] Because G′ k The positive or negative value and its relative size can represent the growth or decline of the data trend at this moment and its relative degree, so the size of the predicted value is correlated with the real-time development trend. k The value is positive and the larger it is, the larger the predicted value is. k The smaller the value of is negative, the smaller the predicted value is. Predicting real-time data can provide early warning for abnormal data and improve the timeliness of maintenance.

[0052] (2) Generally, different monitoring data are compared in real time with the corresponding alarm thresholds set in the control center of the railway station to determine whether to alarm. This application can determine the corresponding alarm triggering degree by comparing the corresponding predicted value with the threshold at the current moment. Since any monitoring data in the computer room exceeds the threshold, the corresponding alarm will be triggered, so it is necessary to take into account the predicted value of each data. Based on the above analysis, the alarm triggering degree of each computer room at each moment is calculated, and the expression is:

[0053] In the formula, is the alarm triggering degree of the a-th computer room at the k-th time; N is the number of types of monitoring data of each computer room, and the value of N in this embodiment is 4; is the predicted value of the b-th monitoring data of the a-th computer room at the k+1th time; μ a,b The preset alarm threshold for the bth type of monitoring data of the ath machine room. The alarm thresholds of various monitoring data can be obtained from the control center of the railway station.

[0054] therefore It represents the difference between the predicted value of the monitoring data and the alarm threshold. If the predicted value is closer to the alarm threshold, the smaller the value is, the higher the alarm triggering degree is. The greater the difference between the predicted value of the monitoring data and the corresponding alarm threshold, the lower the alarm triggering degree is.

[0055] (3) Determine the comprehensive alarm triggering degree of each site at each moment based on the fusion value of the alarm triggering degree of all computer rooms in each site at each moment.

[0056] It should be noted that the fusion described in this application is to combine multiple variables. The specific fusion method can be determined according to actual conditions during the application process, and this application does not impose any special restrictions.

[0057] Preferably, in the embodiment of the present application, the expression of the comprehensive alarm triggering degree of each site is:

[0058] In the formula, is the comprehensive alarm triggering degree of any site at the kth moment, S is the number of computer rooms included in any site, is the alarm triggering degree of the ith computer room in any of the sites at the kth moment, and exp() is an exponential function with the natural number e as the base.

[0059] The above method can be used to calculate the comprehensive alarm triggering degree at the corresponding time for all sites, and then the alarm attribute of the corresponding site is determined based on the comprehensive alarm triggering degree.

[0060] Since the greater the value of the comprehensive alarm triggering degree is, the faster the abnormal maintenance speed of the corresponding site is required to maintain the normal operation of the site, the greater the possibility that it is a critical site, and vice versa, the greater the possibility that it is an ordinary site.

[0061] Step S4, distinguishing between key sites and common sites based on the comprehensive alarm triggering degree of each site; and using alarm mechanisms to monitor and control the key sites and common sites respectively.

[0062] The sites whose normalized values ​​of the comprehensive alarm triggering degree are greater than or equal to the first threshold are regarded as key sites, and the sites whose normalized values ​​of the comprehensive alarm triggering degree are less than the first threshold are regarded as ordinary sites. Preferably, in one embodiment of the present application, the first threshold is set to 0.85. As other embodiments of the present application, the implementer can set the first threshold according to the actual situation.

[0063] The alarm threshold of each monitoring data is obtained through the control center of the railway station. The alarm mechanism of the key station is set to a separate alarm. When the predicted value of each monitoring data in each computer room of the key station at the next moment exceeds the corresponding alarm threshold, the warning information is sent to the key station, so that the warning information can be quickly processed by local maintenance resources to improve the response capability to abnormalities; the alarm mechanism of the ordinary station is set to a centralized alarm. When the predicted value of each monitoring data in each computer room of the ordinary station at the next moment exceeds the corresponding alarm threshold, the warning information is comprehensively analyzed and then fed back to the ordinary station for processing, so as to coordinate maintenance resources to reduce maintenance costs. Then, by adjusting the alarm mechanism of the station, the scheduling of maintenance resources is controlled to realize the monitoring of the power environment of multiple stations.

[0064] The schematic diagram of the process of obtaining the data trend of each monitoring data in each computer room is as follows: Figure 2 shown.

[0065] Based on the same inventive concept as the above method, an embodiment of the present application also provides a multi-site power environment monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned multi-site power environment monitoring methods are implemented.

[0066] In summary, the embodiment of the present application provides a multi-site power environment monitoring method, which performs real-time data prediction by analyzing the monitoring data trends of different sites, determines the comprehensive alarm triggering degree of each site based on the difference between the prediction results and the preset alarm thresholds of various monitoring data, and then sets different alarm strategies for different sites based on the difference in the comprehensive alarm triggering degree by combining individual alarm and centralized alarm methods, determines corresponding alarm mechanisms for different sites for early warning, and optimizes the problem of high management and maintenance costs caused by the inability to perform effective correlation analysis on alarm events of different sites when warning different sites based on individual alarm strategies, ensures the rapid response and autonomy of key sites, and improves the synchronization of unified management and comprehensive analysis of sites, enhances the overall flexibility and management efficiency of site management, and regulates maintenance resources through corresponding alarm mechanisms for warning information of different sites, realizes multi-site power environment monitoring, and avoids the problem of poor power environment monitoring effect due to unreasonable alarm mechanism.

[0067] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0069] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A multi-site power environment monitoring method, characterized in that: The method comprises the following steps: Collect power environment monitoring data of each computer room at each site; Determine the data trend degree of each monitoring data in each computer room based on the change trend of each monitoring data in each computer room; determine the development trend degree of each monitoring data in each computer room at each moment based on the data trend degree of each monitoring data in each computer room and the local data change situation; Determine the predicted value of each monitoring data in each computer room at the next moment based on the numerical value of each monitoring data in each computer room at each moment and the corresponding development trend degree; determine the alarm triggering degree of each computer room at each moment based on the difference between the predicted value and the preset alarm threshold of various monitoring data, and calculate the comprehensive alarm triggering degree of each site; Critical sites and common sites are distinguished based on the comprehensive alarm triggering degree of each site; alarm mechanisms are used to monitor and control critical sites and common sites respectively.

2. A multi-site power environment monitoring method as claimed in claim 1, characterized in that: The process of obtaining the data trend of each monitoring data in each computer room is as follows: For any type of monitoring data of each computer room, the difference between each moment and the previous moment of the monitoring data is calculated and recorded as the first difference; the average of all the first differences of the monitoring data is taken as the data trend of the monitoring data.

3. A multi-site power environment monitoring method as claimed in claim 1, characterized in that: The expression of the development trend degree of each monitoring data of each computer room at each moment is: In the formula, G k is the development trend of any monitoring data in any computer room at the kth moment, A k is the actual value of any of the monitoring data at the kth moment, is the mean of all historical data of any one type of monitoring data before the kth moment, and Y is the data trend of any one type of monitoring data.

4. A multi-site power environment monitoring method as claimed in claim 3, characterized in that: The expression of the predicted value of each monitoring data of each computer room at the next moment is: A′ k+1 =A k ×(1+G′ k ), where A′ k+1 is the predicted value of any monitoring data at the k+1th moment, A k is the actual value of any monitoring data at the kth moment, G′ k It is the normalized value of the development trend degree of any one of the monitoring data at the kth moment.

5. A multi-site power environment monitoring method as claimed in claim 1, characterized in that: The expression of the alarm triggering degree of each computer room at each time is: In the formula, is the alarm triggering degree of the a-th computer room at the k-th time; N is the number of monitoring data types of each computer room; is the predicted value of the b-th monitoring data of the a-th computer room at the k+1th time; μ a,b It is the preset alarm threshold of the bth type of monitoring data in the ath computer room.

6. A multi-site power environment monitoring method as claimed in claim 1, characterized in that: The expression of the comprehensive alarm triggering degree of each site is: The comprehensive alarm triggering degree of each site at each moment is determined based on the fusion value of the alarm triggering degree of all computer rooms in each site at each moment.

7. A multi-site power environment monitoring method as claimed in claim 6, characterized in that: The calculation expression of the comprehensive alarm triggering degree is: In the formula, is the comprehensive alarm triggering degree of any site at the kth moment, S is the number of computer rooms included in any site, is the alarm triggering degree of the ith computer room in any of the sites at the kth moment, and exp() is an exponential function with the natural number e as the base.

8. A multi-site power environment monitoring method as claimed in claim 1, characterized in that: The method of distinguishing key sites from common sites based on the comprehensive alarm triggering degree of each site is specifically as follows: sites whose normalized value of the comprehensive alarm triggering degree is greater than or equal to a preset first threshold are regarded as key sites, and sites whose normalized value of the comprehensive alarm triggering degree is less than the preset first threshold are regarded as common sites.

9. A multi-site power environment monitoring method as claimed in claim 1, characterized in that: The key sites and common sites are monitored and controlled respectively by using alarm mechanisms, specifically: the key sites are monitored and controlled by using separate alarms, and the common sites are monitored and controlled by using centralized alarms.

10. A multi-site power environment monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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